Dr Francisco Perez-Reche

Dr Francisco Perez-Reche

Senior Lecturer

Dr Francisco Perez-Reche
Dr Francisco Perez-Reche

Contact Details


  • Aug 2017 - present. Senior Lecturer. University of Aberdeen.
  • Sept 2012 - Jul 2017. Lecturer. University of Aberdeen.
  • 2011 - Aug 2012. Lecturer. University of Abertay Dundee.
  • 2008 - 2010. Research associate. University of Cambridge.
  • 2006 - 2008. Marie Curie experienced researcher sharing time between Ecole Polytechnique (Paris) and Università di Padova.
  • 2007 - 2008. Postdoctoral Scholar at Université Pierre et Marie Curie.
  • 2005. Ph.D. in Physics. Universitat de Barcelona.

Internal Memberships and Affiliations

  • Aberdeen Group for the Mathematics of Infectious Diseases (AGMID)
  • Director of Undergraduate Pathways in Physics
  • Centre for Bacteria in Health and Disease (CBHD)

Aberdeen Group for the Mathematics of Infectious Diseases


Research Overview

My research has a marked interdisciplinary character which, broadly speaking, uses mathematical modeling and data science as core disciplines to address questions relevant to a broad range of fields including infectious diseases, social contagion, phase transitions and processes in complex networks and disordered systems.

See information on our Aberdeen Group for the Mathematics of Infectious Diseases (AGMID) by clicking here.

Aberdeen Group for the Mathematics of Infectious Diseases

Current Research

Spread and evolution of pathogens

We integrate statistical analysis, mathematical models, genomics and risk assessment to understand the transmission dynamics of bacterial gastrointestinal pathogens such as Campylobacter or Listeria. These pathogens can be found in the environment and are hosted by different types of hosts including humans or animals used for food production (e.g. chickens or salmon).

Work on COVID-19 pandemic

Importance of untested infectious individuals for the suppression of COVID-19 epidemics: We developed compartmental models that incorporate both reported and unreported infectious individuals in the COVID-19 outbreak. These models were used to analyse strategies to suppress the virus in Germany, the Hubei province of China, Italy, Spain and the UK. The research provided evidence to confirm that: (i) more than 50% of infectious individuals were not tested for infection at the early stages of the epidemic, (ii) reducing the underlying transmission of untested cases was crucial to suppress the virus, and (iii) establishing herd immunity was not feasible during the early months of the epidemic. (see paper)

Estimating the number of COVID-19 cases being introduced into UK Higher Education Institutions: We used data on the incidence of COVID-19 from across the world, together with student and staff numbers at UK HEIs in a stochastic mathematical model to predict that 81% of the UK HEIs had more than a 50% chance of having at least one COVID-19 case arriving on campus at the beginning of the 2020-2021 academic year. Predictions for the number of cases expected at each campus were also provided. Based on these estimates, it was suggested that universities had to plan for COVID-19 cases to arrive on campus and facilitate mitigations to reduce the spread of disease particularly during the first two weeks of term. (see paper)

Press coverage: UoA News, STV, NorthSound 1 Radio, The Times, Daily Mail, The Evening Express, The Hippocratic Post, Express and Star, Yahoo UK, MedicalXpress, Gibraltar Chronicle, ...

Machine learning to attribute individuals to sources

Attributing or assigning individuals to a source population is important within many disciplines including ecology, anthropology, infectious diseases and forensics. A common strategy to attribute individuals to populations consists in comparing the genotype of the individual with the genetic profiles of defined source populations. We use data in which individuals of known source are characterised by genetic markers (genes, SNPs, etc) to train classifiers that can be used for source attribution of individuals whose source is unknown. 

Machine learning source attribution

We developed a new machine learning method to attribute individuals to sources: The Minimal Multilocus Distance (MMD) method. This method is computationally fast and can operate on many thousands of genomic markers. In addition, the MMD method is generic, easy to implement for Whole genome sequence (WGS) and proteomic data and has wide application.

Global spread of pathogens

Escherchia coli (STEC) O157:H7 is a zoonotic pathogen that causes numerous food and waterborne disease outbreaks. This pathogen is globally distributed and we explained its origin and the temporal sequence of its geographical spread. This was done by combining Monte Carlo based Bayesian methods with whole genome sequencing (WGS) and geographical information. We found that this virulent pathogen originated in the Netherlands around 1890 and spread around the world by >30 transmission events likely facilitated by farm cattle movements.

  • Learn more: Clinical Infectious Diseases (2019, 2020)

Whole genome sequencing of Listeria

Listeria causes around 2,500 infections and 250 deaths per year in the EU. In a joint project with Statens Serum Institut (SSI), French Agency for Food, Environmental and Occupational Health & Safety (ANSES) and Public Health England (PHE), we used source attribution to identify that the main source of listeriosis was food of bovine origin but there were also contributions from other food animals, including fish.

Biofilm formation

We are developing mathematical models to understand the biofilm formation of foodborne pathogens.


Dynamical processes on complex networks

In the last decades, network science has emerged as a successful field aiming at a systematic understanding of many complex systems. We use network representations of populations of individuals to describe the spread of infection or social phenomena over a wide range of spatial and temporal scales (e.g. from towns and farms to entire countries or the whole world). The main aim is to understand how the mechanisms of transmission of, e.g. infection or ideas, between individuals affect the overall ability of the spreading phenomenon to invade a large portion of the population. This understanding is crucial to optimise the spread of beneficial processes (e.g. good behaviour or news) or prevent spread of damaging processes (e.g. infections or misinformation)

Synergistic transmission and explosive transitions to large social contagion 

We developed compartmental epidemic models to account for local synergistic effects observed in several phenomena including fungal invasion or the spread of social content. For instance, these models revealed that synergistic effects associated with acquaintances of pairs of individuals can explain why both interesting and dull social content can become popular overnight, in a more explosive manner than epidemics. 

Explosive contagion

Explosive immunisation to prevent epidemics by vaccinating as few nodes as possible

We extended the explosive percolation paradigm to develop “explosive immunisation”: a new method to inform targeted immunisation that enables the burden of epidemics to be minimised by vaccinating as few people as possible. The method assigns a score to each individual to quantify their contribution to the chance of a large epidemic if they are not immunised. Explosive immunisation targets individuals with a high score, who were called ‘superblockers’. These are typically well-connected individuals who move between different communities and whose immunisation abruptly decreases the possibility of an epidemic.

Explosive immunisation

Soil biological invasion using network representations for the soil pore space

Soil network


Modelling avalanches

Systems exhibiting a collective avalanche response when driven externally are ubiquitous. Examples of such phenomena include earthquakes, magnetization reversal, structural phase transitions, deformation of living cells, and collective opinion shifts.

Avalanches in living cells

We are developing models to understand the response of living cells when subject to external mechanical stimuli.

Avalanches in structural phase transitions

Many solids undergo complex structural changes when subject to changes of temperature or mechanical load. For instance, this is the case of shape-memory alloys used for medical implants, cars, and many other applications. Such structural changes are associated with phase transitions that typically obey intermittent dynamics characterised by discrete transformation events and intrinsic evolving heterogeneity (e.g. dislocations). Our research uses lattice models derived from continuous theories of mechanics of solids to understand these complex dynamics.

Cycled martensite

Avalanches in disordered systems

The zero-temperature Random Field Ising Model (zt-RFIM) is a prototype model for this class of systems assuming that avalanche behaviour is a consequence of disorder (i.e. heterogeneities of diverse nature) present in the system. Our results are based on exact analytical approaches and numerical simulations.

RFIM bilayer Bethe lattice

Capillary condensation in porous media

Physical systems which consist of networks of pores, such as Vycor, Silica aerogels, porous rocks, soil, and others, have a wide spectrum of applications, ranging from molecular filters and catalysts to fuel storage. Capillary condensation is an important and peculiar physical phenomenon occurring in many such systems. We devise lattice gas models which allow the heterogeneity of porous media to be properly incorporated in the description condensation. 

SBA-15 porous network

Biochar reverse engineering using Machine Learning

Biochar are widely used materials for soil management. The central objective of this research is to determine how the physico-chemical properties of biochar are influenced by the processing parameters (i.e. the starting organic material and pyrolysis temperature). A Biochar Engineering web application was developed to determine suitable preparation strategies to obtain biochars with pre-set properties.

Biochar networks

Colloidal suspensions - Transport in porous media

Colloids play an important role in the transfer of nutrients and pollutants in the environment. We are combining mathematical models and experiments to understand the dynamics of colloidal suspensions in solid surfaces and porous media.  

Stain evaporating dropletMorphologies of stains left by evaporating drops. Different morphologies and attachments are obtained depending on the surface tension 


Teaching Responsibilities

PX3019: Mathematical and computational methods in physics


PX5009: Machine learning

 KNN method


[In previous years...]


PX1016: Understanding the Physical World


PX1019 - Rainbows


ST1506: Understanding data

 Monty Hall


 PX2512: Cosmology, Astronomy and Modern Physics




MX3023: Mechanics A


Non-Course Teaching Responsibilities

Director of Undergraduate Pathways in Physics.



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